{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c6c3b82",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# 中文乱码的处理\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']# 设置微软雅黑字体\n",
    "plt.rcParams['axes.unicode_minus'] = False # 避免坐标轴不能正常的显示负号"
   ]
  },
  {
   "cell_type": "raw",
   "id": "9e6033a8",
   "metadata": {},
   "source": [
    "User_ID      顾客ID\n",
    "Product_ID    商品ID\n",
    "Gender       性别\n",
    "Age         年龄\n",
    "Occupation     职业\n",
    "City_Category    城市 \n",
    "Stay_In_Current_City_Years     在城市的年数\n",
    "Marital_Status         婚姻\n",
    "Product_Category_1      商品类别1\n",
    "Product_Category_2      商品类别2\n",
    "Product_Category_3      商品类别3\n",
    "Purchase             消费金额"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83128730",
   "metadata": {},
   "source": [
    "用户画像是什么？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4158b68",
   "metadata": {},
   "source": [
    "用户画像是真实用户的虚拟代表，是建立在一系列真实数据之上的目标用户模型，它的本质是用户特征的“可视化”，抽象出相应的标签，拟合而成的虚拟的形象，主要包含自然属性、社会属性、行为属性及心理属性。需要注意的是，用户画像是将一类有共同特征的用户聚类分析后得出的，因而并非针对某个具像的特定个人。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1372b57e",
   "metadata": {},
   "source": [
    "用户画像用处\n",
    "+ 帮助我们更加立体的认识用户，洞察用户的需求，从而优化完善产品，提升用户体验\n",
    "+ 帮我我们更加精准进行决策，通过市场数据推论到产品的人群定位，做到精细化运营"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "402d1d9d",
   "metadata": {},
   "source": [
    "# 项目说明"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e57b4e10",
   "metadata": {},
   "source": [
    "本案例使用的是，亚马逊黑色星期五用户购物的数据集，通过分析用户的行为，来刻画出优质客户的画像，从而找出高质量用户"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e413bd4d",
   "metadata": {},
   "source": [
    "# 提出问题"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69dbcea1",
   "metadata": {},
   "source": [
    "通过用户画像找出高质量消费用户群体，对高消费用户进行个性化定制服务，从而提升用户体验和用户忠诚度"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c632adc6",
   "metadata": {},
   "source": [
    "**用户画像**\n",
    "+ 用户基本属性\n",
    "    + 性别\n",
    "    + 年龄\n",
    "    + 职业\n",
    "    + 城市\n",
    "    + 婚姻\n",
    "+ 用户行为属性\n",
    "    + 在城市的年数\n",
    "    + 购买的商品\n",
    "    + 商品的类别\n",
    "    + 消费金额"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "725c753c",
   "metadata": {},
   "source": [
    "# 数据整理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4cc97959",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('D:/数据项目/黑五购物数据集用户画像/BlackFriday.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c27f4792",
   "metadata": {},
   "source": [
    "字段解释"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c3055b",
   "metadata": {},
   "source": [
    "+ User_ID      顾客ID\n",
    "+ Product_ID    商品ID\n",
    "+ Gender       性别\n",
    "+ Age         年龄\n",
    "+ Occupation     职业\n",
    "+ City_Category    城市 \n",
    "+ Stay_In_Current_City_Years     在城市的年数\n",
    "+ Marital_Status         婚姻\n",
    "+ Product_Category_1      商品类别1\n",
    "+ Product_Category_2      商品类别2\n",
    "+ Product_Category_3      商品类别3\n",
    "+ Purchase             消费金额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ebafd906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00248942</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00087842</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00085442</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000002</td>\n",
       "      <td>P00285442</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>16</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
       "0  1000001  P00069042      F  0-17          10             A   \n",
       "1  1000001  P00248942      F  0-17          10             A   \n",
       "2  1000001  P00087842      F  0-17          10             A   \n",
       "3  1000001  P00085442      F  0-17          10             A   \n",
       "4  1000002  P00285442      M   55+          16             C   \n",
       "\n",
       "  Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                          2               0                   3   \n",
       "1                          2               0                   1   \n",
       "2                          2               0                  12   \n",
       "3                          2               0                  12   \n",
       "4                         4+               0                   8   \n",
       "\n",
       "   Product_Category_2  Product_Category_3  Purchase  \n",
       "0                 NaN                 NaN      8370  \n",
       "1                 6.0                14.0     15200  \n",
       "2                 NaN                 NaN      1422  \n",
       "3                14.0                 NaN      1057  \n",
       "4                 NaN                 NaN      7969  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78ffa384",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 537577 entries, 0 to 537576\n",
      "Data columns (total 10 columns):\n",
      " #   Column                      Non-Null Count   Dtype \n",
      "---  ------                      --------------   ----- \n",
      " 0   User_ID                     537577 non-null  int64 \n",
      " 1   Product_ID                  537577 non-null  object\n",
      " 2   Gender                      537577 non-null  object\n",
      " 3   Age                         537577 non-null  object\n",
      " 4   Occupation                  537577 non-null  int64 \n",
      " 5   City_Category               537577 non-null  object\n",
      " 6   Stay_In_Current_City_Years  537577 non-null  object\n",
      " 7   Marital_Status              537577 non-null  int64 \n",
      " 8   Product_Category_1          537577 non-null  int64 \n",
      " 9   Purchase                    537577 non-null  int64 \n",
      "dtypes: int64(5), object(5)\n",
      "memory usage: 41.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# 由于 Product_Category_2  Product_Category_3 缺失值较多 而且没有具体说明不清楚有什么代表性 可以删除\n",
    "data = data.drop(columns=['Product_Category_2','Product_Category_3'])\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87e2e4a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看有无重复值\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4efaf422",
   "metadata": {},
   "source": [
    "## 用户基本属性"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde9690b",
   "metadata": {},
   "source": [
    "### 性别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53a33540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'M'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检查性别类型\n",
    "data.Gender.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ad8334db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    405380\n",
       "F    132197\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bea9aba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    4225\n",
       "F    1666\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用户ID去重后统计男女比例\n",
    "gender = data.groupby('User_ID')['Gender'].max().value_counts()\n",
    "gender_Purchase = data.groupby('Gender')['Purchase'].sum().sort_values(ascending=False)\n",
    "gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b3abdc1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.subplot(1,2,1)\n",
    "plt.pie(gender,labels=['男','女'],autopct='%.1f%%',textprops = {'fontsize':20, 'color':'k'})\n",
    "plt.title('性别占比',size=20)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.pie(gender_Purchase,labels=['男','女'],autopct='%.1f%%',textprops = {'fontsize':20, 'color':'k'})\n",
    "plt.title('不同性别消费总金额',size=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45607306",
   "metadata": {},
   "source": [
    "从性别数量占比和性别消费占比来看，黑五活动主要购买消费力为男性，数量和消费能力都达到70%以上"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e0602b",
   "metadata": {},
   "source": [
    "### 年龄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b0be3b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "age = data.groupby('User_ID')['Age'].max()\n",
    "age = age.value_counts()\n",
    "age = age.reset_index()\n",
    "\n",
    "age_Purchase = data.groupby('Age')['Purchase'].sum().sort_values(ascending=False)\n",
    "age_Purchase = age_Purchase.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bcab4052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26-35</td>\n",
       "      <td>1999749106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36-45</td>\n",
       "      <td>1010649565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18-25</td>\n",
       "      <td>901669280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46-50</td>\n",
       "      <td>413418223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51-55</td>\n",
       "      <td>361908356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55+</td>\n",
       "      <td>197614842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0-17</td>\n",
       "      <td>132659006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age    Purchase\n",
       "0  26-35  1999749106\n",
       "1  36-45  1010649565\n",
       "2  18-25   901669280\n",
       "3  46-50   413418223\n",
       "4  51-55   361908356\n",
       "5    55+   197614842\n",
       "6   0-17   132659006"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_Purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "43f14242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '各年龄段消费情况')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.subplot(1,2,1)\n",
    "color = ['lightcoral','palegreen','lightblue','orange','tomato','pink']\n",
    "plt.bar(age['index'], age.Age, color =color, alpha=0.8)\n",
    "plt.title('各年龄段统计情况',size=20)\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "color = ['lightcoral','palegreen','lightblue','orange','tomato','pink']\n",
    "plt.bar(age_Purchase['Age'], age_Purchase.Purchase, color =color, alpha=0.8)\n",
    "plt.title('各年龄段消费情况',size=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a87d5c3f",
   "metadata": {},
   "source": [
    "用户数量和消费能力都集中在26-35岁的年龄区间，和36-45,18-25之间，看得出都是中青年人群为主要购买力消费人群"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa0c7489",
   "metadata": {},
   "source": [
    "### 职业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "927ed650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Occupation = data.groupby('User_ID')['Occupation'].max()\n",
    "Occupation = Occupation.value_counts()\n",
    "Occupation = Occupation.reset_index()\n",
    "Occupation\n",
    "\n",
    "Occupation_Purchase = data.groupby('Occupation')['Purchase'].sum().sort_values(ascending=False)\n",
    "Occupation_Purchase = Occupation_Purchase.reset_index()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(16,10))\n",
    "plt.subplot(2,1,1)\n",
    "plt.bar(Occupation['index'], Occupation.Occupation, alpha=0.8,color='lightgreen')\n",
    "plt.xticks(Occupation['index'])\n",
    "plt.title('各职业人数统计',size=20)\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.bar(Occupation_Purchase['Occupation'], Occupation_Purchase.Purchase, alpha=0.8,color='lightgreen')\n",
    "plt.xticks(Occupation_Purchase['Occupation'])\n",
    "plt.title('各职业消费情况统计',size=20)\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc4b307",
   "metadata": {},
   "source": [
    "可以看出各职业的人数和消费情况分布基本一样，主要集中在 0职业，4职业，7职业 这三个职业  4职业最多，占主要购买力的职业为4职业"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c00b480e",
   "metadata": {},
   "source": [
    "### 城市"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cdb374e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "City_Category = data.groupby('User_ID')['City_Category'].max()\n",
    "City_Category = City_Category.value_counts()\n",
    "City_Category = City_Category.reset_index()\n",
    "\n",
    "City_Category_Purchase = data.groupby('City_Category')['Purchase'].sum().sort_values(ascending=False)\n",
    "City_Category_Purchase = City_Category_Purchase.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74f5c28b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '各城市用户消费情况')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = ['lightcoral','palegreen','lightblue']\n",
    "plt.figure(figsize=(16,7))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.bar(City_Category['index'], City_Category.City_Category, color =color, alpha=0.8)\n",
    "plt.title(\"各城市用户数统计\",size=20)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.bar(City_Category_Purchase['City_Category'], City_Category_Purchase.Purchase, color =color, alpha=0.8)\n",
    "plt.title('各城市用户消费情况',size=20)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75a212da",
   "metadata": {},
   "source": [
    "消费者来C城市的最多，比A,B两个城市的综合还要多，但是消费能类最高的是B城市，所以B城市的用户的消费能力最高"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4309255",
   "metadata": {},
   "source": [
    "### 婚姻"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "21521228",
   "metadata": {},
   "outputs": [],
   "source": [
    "Marital_Status = data.groupby('User_ID')['Marital_Status'].max()\n",
    "Marital_Status = Marital_Status.value_counts()\n",
    "Marital_Status = Marital_Status.reset_index()\n",
    "\n",
    "Marital_Status_Purchase = data.groupby('Marital_Status')['Purchase'].sum().sort_values(ascending=False)\n",
    "Marital_Status_Purchase = Marital_Status_Purchase.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6ab8cfe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = ['lightcoral','lightgreen']\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.subplot(1,2,1)\n",
    "plt.pie(Marital_Status.Marital_Status,labels=['未婚','已婚'],autopct='%.1f%%',colors=color,textprops = {'fontsize':20, 'color':'k'})\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.pie(Marital_Status_Purchase.Purchase,labels=['未婚人群消费','已婚人群消费'],autopct='%.1f%%',colors=color,textprops = {'fontsize':20, 'color':'k'})\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f917fdb1",
   "metadata": {},
   "source": [
    "人数占比和消费占比基本上差不多，未婚的用户占58%，已婚的用户占42%，未婚用户高于已婚用户的数量，主要购买力为未婚人群"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a80219b",
   "metadata": {},
   "source": [
    "总结： 从用户基本属性看出，费用户多为来自C城市的男性，年龄在26-35岁左右，未婚，主要从事0职业，4职业，7职业"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef97630",
   "metadata": {},
   "source": [
    "## 用户行为属性"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4a7a70e",
   "metadata": {},
   "source": [
    "### 在城市的年数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb9159c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Stay_In_Current_City_Years = data.groupby('User_ID')['Stay_In_Current_City_Years'].max()\n",
    "Stay_In_Current_City_Years = Stay_In_Current_City_Years.value_counts()\n",
    "Stay_In_Current_City_Years = Stay_In_Current_City_Years.reset_index()\n",
    "\n",
    "Stay_In_Current_City_Years_Purchase = data.groupby('Stay_In_Current_City_Years')['Purchase'].sum().sort_values(ascending=False)\n",
    "Stay_In_Current_City_Years_Purchase = Stay_In_Current_City_Years_Purchase.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7d3b06b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = ['lightcoral','palegreen','lightblue','orange','tomato','pink']\n",
    "plt.figure(figsize=(16,6))\n",
    "plt.subplot(1,2,1)\n",
    "plt.bar(Stay_In_Current_City_Years['index'], Stay_In_Current_City_Years.Stay_In_Current_City_Years, color =color, alpha=0.8)\n",
    "plt.title('居住时间用户数统计',size=20)\n",
    "\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.bar(Stay_In_Current_City_Years_Purchase['Stay_In_Current_City_Years'], Stay_In_Current_City_Years_Purchase.Purchase, color =color, alpha=0.8)\n",
    "plt.title('居住时间消费金额',size=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8c7aec",
   "metadata": {},
   "source": [
    "居住时间的人数占比和消费能力占比基本一样，居住时间在1年的用户购买最高，有可能是因为刚在这座城市稳定下来需要购置长期的生活用品的原因"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa17ff79",
   "metadata": {},
   "source": [
    "### 购买最多的商品的类别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "05862edc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>148592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>138353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>112132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>23960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Product_ID\n",
       "Product_Category_1            \n",
       "5                       148592\n",
       "1                       138353\n",
       "8                       112132\n",
       "11                       23960\n",
       "2                        23499"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Product_Category_1 = data.groupby('Product_Category_1')['Product_ID'].count().sort_values(ascending=False)\n",
    "Product_Category_1 = Product_Category_1[:10]\n",
    "# print('购买最多的商品类别1：\\n',Product_Category_1[:1])\n",
    "pd.DataFrame(Product_Category_1)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80a0651a",
   "metadata": {},
   "source": [
    "购买最多的商品的类别为 Product_Category_1=5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3259fd91",
   "metadata": {},
   "source": [
    "### 购买最多的商品"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3af4bcc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P00265242</th>\n",
       "      <td>1858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P00110742</th>\n",
       "      <td>1591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P00025442</th>\n",
       "      <td>1586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P00112142</th>\n",
       "      <td>1539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P00057642</th>\n",
       "      <td>1430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Product_ID\n",
       "Product_ID            \n",
       "P00265242         1858\n",
       "P00110742         1591\n",
       "P00025442         1586\n",
       "P00112142         1539\n",
       "P00057642         1430"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Product_ID = data.groupby('Product_ID')['Product_ID'].count().sort_values(ascending=False)\n",
    "# print('购买前五的商品ID：\\n',Product_ID[:5])\n",
    "pd.DataFrame(Product_ID)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16455951",
   "metadata": {},
   "source": [
    "## 用户的整体性画像描述\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39cc2eb2",
   "metadata": {},
   "source": [
    "**高质量消费用户的特征:**\n",
    "\n",
    "+ 多为来自B城市的男性，\n",
    "+ 年龄在26-35岁左右，\n",
    "+ 未婚，\n",
    "+ 主要从事4职业，\n",
    "+ 来自B城市，\n",
    "+ 居住一年以内刚稳定下来的客户\n",
    "+ 购买最多的商品类型为 Product_Category_1=5\n",
    "+ 购买最多的商品为 P00265242"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d25af7f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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